code: def _mifid_compute(
      mu1: Tensor,
      sigma1: Tensor,
      features1: Tensor,
      mu2: Tensor,
      sigma2: Tensor,
      features2: Tensor,
      cosine_distance_eps: float = 0.1,
  ) -> Tensor:
      """Compute MIFID score given two sets of features and their statistics."""
      fid_value = _compute_fid(mu1, sigma1, mu2, sigma2)
      distance = _compute_cosine_distance(
          features1, features2, cosine_distance_eps
      )
      # secure that very small fid values does not explode the mifid
      return (
          fid_value / (distance + 10e-15)
          if fid_value > 1e-8
          else torch.zeros_like(fid_value)
      )
  
file_path: torchmetrics/src/torchmetrics/image/mifid.py
imports: from typing import Any, List, Optional, Sequence, Union
  import torch
  from torch import Tensor
  from torch.nn import Module
  from torchmetrics.image.fid import NoTrainInceptionV3, _compute_fid
  from torchmetrics.metric import Metric
  from torchmetrics.utilities.data import dim_zero_cat
  from torchmetrics.utilities.imports import _MATPLOTLIB_AVAILABLE, _TORCH_FIDELITY_AVAILABLE
  from torchmetrics.utilities.plot import _AX_TYPE, _PLOT_OUT_TYPE
label_desc: Use image node_id 2 for calling a global function with example usage: _mifid_compute(mu1, sigma1, features1, mu2, sigma2, features2, cosine_distance_eps) and returns: unknown